Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data
Natural source electromagnetic methods have the potential to recover rock property distributions from the surface to great depths. Unfortunately, results in complex 3D geo-electrical settings can be disappointing, especially where significant near-surface conductivity variations exist. In such setti...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
Springer
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/4215 |
| _version_ | 1848744453159780352 |
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| author | Le, C. Harris, Brett Pethick, A. Takam Takougang, Eric Howe, B. |
| author_facet | Le, C. Harris, Brett Pethick, A. Takam Takougang, Eric Howe, B. |
| author_sort | Le, C. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Natural source electromagnetic methods have the potential to recover rock property distributions from the surface to great depths. Unfortunately, results in complex 3D geo-electrical settings can be disappointing, especially where significant near-surface conductivity variations exist. In such settings, unconstrained inversion of magnetotelluric data is inexorably non-unique. We believe that: (1) correctly introduced information from seismic reflection can substantially improve MT inversion, (2) a cooperative inversion approach can be automated, and (3) massively parallel computing can make such a process viable. Nine inversion strategies including baseline unconstrained inversion and new automated/semiautomated cooperative inversion approaches are applied to industry-scale co-located 3D seismic and magnetotelluric data sets. These data sets were acquired in one of the Carlin gold deposit districts in north-central Nevada, USA. In our approach, seismic information feeds directly into the creation of sets of prior conductivity model and covariance coefficient distributions.We demonstrate how statistical analysis of the distribution of selected seismic attributes can be used to automatically extract subvolumes that form the framework for prior model 3D conductivity distribution. Our cooperative inversion strategies result in detailed subsurface conductivity distributions that are consistent with seismic, electrical logs and geochemical analysis of cores. Such 3D conductivity distributions would be expected to provide clues to 3D velocity structures that could feed back into full seismic inversion for an iterative practical and truly cooperative inversion process. We anticipate that, with the aid of parallel computing, cooperative inversion of seismic and magnetotelluric data can be fully automated, and we hold confidence that significant and practical advances in this direction have been accomplished. |
| first_indexed | 2025-11-14T06:01:42Z |
| format | Journal Article |
| id | curtin-20.500.11937-4215 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:01:42Z |
| publishDate | 2016 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-42152017-09-13T14:44:35Z Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data Le, C. Harris, Brett Pethick, A. Takam Takougang, Eric Howe, B. Natural source electromagnetic methods have the potential to recover rock property distributions from the surface to great depths. Unfortunately, results in complex 3D geo-electrical settings can be disappointing, especially where significant near-surface conductivity variations exist. In such settings, unconstrained inversion of magnetotelluric data is inexorably non-unique. We believe that: (1) correctly introduced information from seismic reflection can substantially improve MT inversion, (2) a cooperative inversion approach can be automated, and (3) massively parallel computing can make such a process viable. Nine inversion strategies including baseline unconstrained inversion and new automated/semiautomated cooperative inversion approaches are applied to industry-scale co-located 3D seismic and magnetotelluric data sets. These data sets were acquired in one of the Carlin gold deposit districts in north-central Nevada, USA. In our approach, seismic information feeds directly into the creation of sets of prior conductivity model and covariance coefficient distributions.We demonstrate how statistical analysis of the distribution of selected seismic attributes can be used to automatically extract subvolumes that form the framework for prior model 3D conductivity distribution. Our cooperative inversion strategies result in detailed subsurface conductivity distributions that are consistent with seismic, electrical logs and geochemical analysis of cores. Such 3D conductivity distributions would be expected to provide clues to 3D velocity structures that could feed back into full seismic inversion for an iterative practical and truly cooperative inversion process. We anticipate that, with the aid of parallel computing, cooperative inversion of seismic and magnetotelluric data can be fully automated, and we hold confidence that significant and practical advances in this direction have been accomplished. 2016 Journal Article http://hdl.handle.net/20.500.11937/4215 10.1007/s10712-016-9377-z Springer unknown |
| spellingShingle | Le, C. Harris, Brett Pethick, A. Takam Takougang, Eric Howe, B. Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data |
| title | Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data |
| title_full | Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data |
| title_fullStr | Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data |
| title_full_unstemmed | Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data |
| title_short | Semiautomatic and Automatic Cooperative Inversion of Seismic and Magnetotelluric Data |
| title_sort | semiautomatic and automatic cooperative inversion of seismic and magnetotelluric data |
| url | http://hdl.handle.net/20.500.11937/4215 |